Tracking Large-Scale Video Remix in Real-World Events

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Abstract

Content sharing networks, such as YouTube, contain traces of both explicit online
interactions (such as likes, comments, or subscriptions), as well as latent
interactions (such as quoting, or remixing, parts of a video). We propose visual
memes, or frequently re-posted short video segments, for detecting and monitoring
such latent video interactions at scale. Visual memes are extracted by scalable
detection algorithms that we develop, with high accuracy. We further augment visual
memes with text, via a statistical model of latent topics. We model content
interactions on YouTube with visual memes, deﬁning several measures of inﬂuence and
building predictive models for meme popularity. Experiments are carried out with
over 2 million video shots from more than 40,000 videos on two prominent news
events in 2009: the election in Iran and the swine ﬂu epidemic. In these two
events, a high percentage of videos contain remixed content, and it is apparent
that traditional news media and citizen journalists have different roles in
disseminating remixed content. We perform two quantitative evaluations for
annotating visual memes and predicting their popularity. The proposed joint
statistical model of visual memes and words outperforms an alternative concurrence
model, with an average error of 2% for predicting meme volume and 17% for
predicting meme lifespan.